Emerging Technology
Special Interest Group
Aug 10th, 2023
Please add your aendance to:
hps://github.com/nos/zenith/issues/69
Agenda
Announcements
Deep Dives
Arcial Intelligence Primers
POC Program
Any Other Admin
Call to Acon
Any Other Business
Thanks & Close-Out
Announcements
Announcements
Blogs
Analog Iterative Machine's lightning-fast approach to
optimization
DeepSpeed ZeRO++, a leap in speed for LLM and chat
model training with 4X less communication
Microsoft announces NVD5
Announcements
Upcoming Events
Thursday, Aug 10 (Today!)
11am EST / 4pm BST
FDC3: Web Browsers
11pm EST / 4pm BST
Morphir
hps://www.nos.org/news-and-events
September 19 - Linux Foundaon Open
Source Summit Europe
Open Source Summit is the premier event for open source
developers, technologists, and community leaders to collaborate,
share informaon, solve problems, and gain knowledge,
furthering open source innovaon and ensuring a sustainable
open source ecosystem. It is the gathering place for open-source
code and community contributors. Register here.
November 1
Open Source in Finance Forum - NYC
Registraon is open for our annual Open Source in Finance
Forum in the Marrio Marquis Hotel in Times Square NYC. Find
informaon on how to sponsor or register here.
Announcements
Upcoming Events
All through August
🏆 Prizes:
Exclusive Interview:
There's a chance to get interviewed by Sal
Kimmich, a known leader in Open Source, AI and
DevOps, working with site reliability engineers
and cybersecurity specialists to implement best
tools and practices to remove toil from developer
workflows.
Showcase Your Ideas:
The top entries will be featured in Gadfly AI's
Cyberscape Zine, giving you exposure in the AI
and tech community!
How to Parcipate:
1. Write a story about AI on Hackernoon.com.
2. Add #future-of-AI to your entry.
3. Share your thoughts on the future of AI.
🚀 Topics to Explore:
How AI is changing creavity and art.
The blend of AI and generave art + code.
Ethical and regulatory aspects of AI.
Latest AI research insights.
Embrace the transformave power of AI.
Dive into AI security discussions.
Sal Kimmich
Director of
Open Source,
Escher Cloud
Deep Dive – Arcial Intelligence
Primers
bit.ly/zenith-primers
Next Primers
Generave AI
Data Annotaon
Data De-Idencaon
AI Chipsets
Specialized processors
designed to accelerate AI
computations, enabling
faster and more efficient AI
model training and inference.
In fintech, AI chipsets drive
groundbreaking
advancements, powering
complex algorithms for fraud
detection, risk assessment,
and personalized financial
recommendations.
The high-performance
computing capabilities of AI
chipsets empower fintech
companies to deliver real-
time, data-intensive services,
transforming the way
financial institutions operate
and serve their customers.
You can find out more about
this subject in our AI Chipset
Primer on the Zenith GitHub.
8
Surging Demand
for applications
Deep learning
advancements
Need for
fast/ecient data
processing
Regulatory pressure to
improve fraud detecon
Cost savings through AI-
driven automation
Integraon across mulple
processes
AI-powered data analytics
Desire to enhance financial
inclusivity & accessibility
Legacy hardware
manufacturers
Tradional nancial services
requiring adaptaon
Skill shis in job market as AI
impacts workforce
Faster & more accurate decision making
Risk mgmt. & fraud detection capabilities
Real-me data processing for insights
Optimised AI model training on chipsets
AI-powers Fintech expands market reach
Increased investment for innovative startups
Data storage and integraon chipsets
Collaboraon with data/chipset manufacturers
Disrupon to trad. hardware supply chains
Success of
accelerated AI
model training
Scalability of AI
chipsets for high
volume data
Need for continuous upskilling to ensure
workforce proficiency
Growing ecosystem
of service providers
Availability of dev
tools & frameworks
Need for connuous upskilling to ensure
chipsets are implemented correctly
Data privacy
concerns
Integraon
challenges with
legacy hardware
Regulatory compliance complexities
Short term
Inial adopon and
integraon of chipsets
Medium term
Widespread deployment
in financial services
Long term
Evolution of chipsets for
autonomous solutions
Potential delays in
advancements and
innovations
Market resistance and
scepcism in AI-decision
making
Ethical consideraons
surrounding responsible
use
AI chipsets oer a compelling opportunity for ntech, revoluonizing nancial services through accelerated AI computaons. Advancements in specialized hardware enable groundbreaking
applicaons in fraud detecon, risk assessment, and personalized nancial recommendaons. The high-performance compung capabilies of AI chipsets empower real-me, data-intensive
nancial services, transforming tradional banking and ushering in a new era of customer-centric nancial experiences. Despite technical feasibility and potenal nancial benets, challenges like
data privacy, regulatory compliance, and integraon fricons require careful navigaon. With a promising future outlook, early adopon and collaboraon between ntech players and AI chipset
manufacturers will play a crucial role in shaping the trajectory of AI-driven ntech innovaon.
AI-Driven
Fraud Detection
This leverages advanced
machine learning algorithms
to detect and prevent
fraudulent activities in real-
time.
In fintech, this technology
acts as a vigilant security
layer, continuously analysing
vast volumes of financial
data to identify suspicious
patterns and transactions.
By swiftly detecting and
mitigating fraud, AI-driven
systems protect financial
assets, preserve customer
trust, and enhance overall
cybersecurity in the rapidly
evolving digital financial
landscape.
You can find out more about
this subject in our AI Chipset
Primer on the Zenith GitHub.
5
Escalating cyber
threats
AI advancements
increase accuracy
& efficiency
Growing adoption
of digital financial
services
Regulatory mandates &
compliance reqs.
Signicant cost
saving through
loss prevenon
Brand reputaon
& Consumer trust
Widespread adoption leads
to collective intelligence
Collaborave Industry-wide
defence strategy
Increased trust in FS
promotes further adoption
Cybercriminals facing more
evolved systems
Legacy fraud detection
losing competitiveness
Shi in the dynamics of
nancial crime invesgaon
toward automaon
Superior fraud detecon & reduced losses
Enhanced customer trust & loyalty
Real-time response capabilities to incidents
Cost savings & less post-fraud invesgaons
Customer retention through reputation
Investments & partnerships for potential
applications following successful tests
Integration of AI-driven solutions to core infra
Collaboraon between AI soluon providers,
data aggregators & nancial enes
Potenal changes in risk assessment and
underwring of Financial Services
Demonstrable
success in real world
scenarios
Scalability of AI
models to process
vast volumes of data
Connuous improvement and renement of ML
models to adapt to evolving threat landscape
Readily available
dev. frameworks
Growing ecosystem
of talent working
with AI tools
Need for continuous upskilling to keep up with
emerging fraud techniques
Data privacy
concerns
Over-reliance on AI
models leading to
blind spots
Regulatory compliance complexies in
explainability of models
Short term
Integration efforts into
security infrastructure
Medium term
Industry-wide adopon &
increased reliance
Long term
Evolution of AI-driven
responses to emerging
fraud techniques
False negatives where AI
fails to detect new or
adaptive fraud patterns
Adversarial attacks
targeting AI models to
manipulate outcomes
Potenal overng or
bias in AI models
aecng accuracy and/or
fairness of detecon
AI-driven fraud detecon represents a signicant opportunity in ntech, providing real-me protecon against sophiscated cyber threats. Advanced machine learning algorithms analyse vast
nancial data to swily idenfy and prevent fraudulent acvies, safeguarding nancial assets and customer trust. Collaboraon between ntech companies, nancial instuons, and
cybersecurity experts is driving the development and adopon of robust fraud prevenon soluons. The technology's impact is far-reaching, with macro network eects and improved
cybersecurity across the digital nancial landscape. While the potenal for nancial benets and compeve advantages is substanal, the implementaon of AI-driven fraud detecon requires
careful consideraon of technical feasibility, fricons, and risks. By striking the right balance between innovaon and responsible use, AI-driven fraud detecon will connue to transform the way
nancial enes combat nancial crime, contribung to a more secure and trusted nancial ecosystem.
Computer Vision
An AI technology that
enables machines to
interpret and understand
visual informaon.
In ntech, computer vision
revoluonizes various
processes, from automang
document vericaon and
identy recognion to
analysing nancial charts and
visualizing data paerns.
By harnessing the power of
computer vision, ntech
companies streamline
operaons, enhance user
experiences, and unlock
valuable insights from visual
data, driving eciency and
innovaon.
You can nd out more about
this subject in our AI Chipset
Primer on the Zenith GitHub.
Increasing
availability of HQ
visual data
Growing demand
for automaon &
eciency
Rising interest in
data-driven FS
decision making
Competitive advantage
through visualising
complex data
Potential
improvements to
fraud detection
Need for fast &
accurate ID & docs
vericaon
More focus on visual
interacon with data
Combinaon of computer
vision with other AI tech
Improved accessibility &
inclusivity via automation
Obsolescence creates need
to adapt to automaon
Conventional data analysis
methods challenged
Shift in the skill set required
within FS Operational tasks
Quick & seamless document processing
Enhanced security through adv. vericaon
Data-driven decision making for strategy
Automaon of labour intensive doc vericaon
Higher customer engagement & satisfaction
Demonstraon of technological leadership &
innovaon of products with customer focus
Integration of end-to-end operations
Collaboration required to meet specific
industry needs
Streamlining of data collection & analysis
Real-world demos of
visual data
interpretaon
Scalability to handle
large-scale real-me
data
Connuous improvement and renement
required to improve accuracy & eciency
Readily available
libraries, APIs and
tools
Growing ecosystem
of collaborang
enes
Need for continuous upskilling to keep up with
new data use cases
Data privacy
concerns over
processing
Ethical
considerations when
decision making
Potenal biases aecng nancial data analysis
& decision outcomes
Short term
Document vericaon
and basic data analysis
Medium term
Widespread POCs for
other FS operaons
Long term
Augmented Reality based
financial interactions
Technical challenges
aecng decision making
Regulatory compliance in
ID verification &
customer data processing
Market resistance to
automated interpretation
of visual data
Computer vision technology holds immense potential in transforming fintech processes, enabling machines to interpret and understand visual information. Fintech companies are leveraging
computer vision to automate document verification, enhance fraud detection, and gain valuable insights from complex financial data. The technology's impact extends to improved user
experiences, data-driven decision-making, and increased efficiency in financial operations. While the opportunity is significant, challenges related to technical feasibility, ethical considerations,
and regulatory compliance must be addressed. The future outlook for computer vision in fintech is promising, with a gradual timeline for adoption and a need for careful risk management and
responsible implementation. By capitalizing on the benefits and addressing frictions, computer vision will play a pivotal role in driving innovation and efficiency in the evolving landscape of
fintech services.
9
Arcial Intelligence Primer
Proof of Concepts
Test out new technologies
Leo
Mordasini
POC Program
Co-ordinator
Exploraon program within the SIG aiming to test concepts and
create new projects
Enables innovators to be able to pursue conceptual designs and ideas
with the proper resources and sponsorship
Control gates on the process to manage the flow of funding and duration
of exploration so that we can fail fast and win quickly.
Projects subject to a vetting process
What is a Proof of Concept (PoC)
Phase 1: Ideaon
Crowdsource for ideas on how to solve the biggest blockers with our primers
Phase 2: Proposal
Aer gathering feedback from the open-source community, ocially pitch your idea
for approval, funding and sponsorship
Phase 3: POC Kicko
Provide the environment and tools needed to successfully facilitate exploraon of
ideas
Phase 4: Demo
Showcase wins/loses and contribute insights back to the community
POC Process Overview
Ideation Proposal Kickoff Demo
Phase 1:
Ideation
Opportunity Outline
What is a Primer
Created by subject maer experts within
the Brain Trust
Provides an introducon to the subject &
relevant reading to seed knowledge
Open for comments for further discussion &
follow-ups
Outline where disrupon is possible or
worth exploring
Current blockers are called out to aid exploraon
in our POC program
Ideaon Proposal Kicko Demo
Call for Action Discuss your ideas!
Opportunity is posted in
the Zenith repo when
available
Utilize GitHub Discussions
to facilitate ideation
amongst the community
Ideaon Proposal Kicko Demo
Submission of Idea
Innovators submit a PR to the Zenith GitHub
repository in the respective opportunity folder
Pull Request is the submission of the idea, based
on a PR template provided
Template will include all the questions the
submitter needs to answer as part of the idea
submission
Ideation Proposal Kicko Demo
SME Veng & Feedback
Sanity check by the Brain Trust to confirm
ideas are:
Possible
Feasible
Worthwhile
Legal
Not already explored
At least 3 approvals from the brain trust for an
idea to move to the value proposition phase
Fully automated via GitHub to not add
unnecessary steps and ensure a seamless
onboarding process
Ideaon Proposal Kickoff Demo
Phase 2:
Proposal
Pitch Value Proposion
Partner with the Brain Trust
You will be partnered with a mentor from the
Brain Trust who will help your team put together
the pitch value proposion
Create a video proposal
Your team will have the opportunity to create a
proposal for project approval, funding and
requesng addional resources.
This approach removes FINOS from being a
boleneck when reviewing ideas
Ideation Proposal Kickoff Demo
Funding Governance
A pool of representaves from FINOS
vote to approve access to exploraon
funds or resources to facilitate
exploraon
Funding would have a per-project cap
on both budget and duraon
Projects without funding asks can skip
this step
Ideaon Proposal Kickoff Demo
Further phases to
be covered in a
future session
Call to Action!
First three deep dives have been published!
AI Chipsets
AI-Driven Fraud Detecon
Computer Vision
Ideaon Proposal Kickoff Demo
Ideation Proposal Kickoff Demo
Start collaborang in Discussions!
Ulize Discussion tools to
collaborate with the open source
community:
General Discussion
Ideas
Polls
Q&A
Show and Tell
Any Other Admin
Please add your
aendance to this
call!
hps://github.com/no
s/zenith/issues/69
Join our mailing list
for future updates
(You don’t need to put anything in
the message)
Call to Action
Get in touch with us
through the mail group
Go an add your
comments and addions
to the AI Primers!
Let us know if youd like
a spotlight!
Join our Discussions
and submit POC ideas
Any Other Business?
Thank you
Join the discussion at
zenith.finos.org